Acoustic environments provide many valuable cues for context aware computing applications. From the acoustic environment we can infer the types of activity, communication modes and other actors involved in the activity. The datasets we collected are all available here.

Our initial experiments were conducted with high quality mono sound recordings made in the Norwich area (with the exception of some of the football match recordings) during the spring and summer of 2002.

For the second, low bandwidth, series of experiments we used an MP3 recorder attached to the strap of a shoulder bag as the recording device to capture the environmental noise from a typical daily routine. The recordings were made in and around Norwich area during the spring and summer of 2004. In each environment we conducted several recording sessions to gather a range of data at different times in similar places.

Environmental or background noise can be classified with a high degree of accuracy using recordings from microphones commonly found in PDAs and other consumer devices.

Environmental noise data sets

Series 1 was recorded using a Sony MiniDisk recorder and external microphone in 2002.
Sampling rate: WAV 22.050kHZ 16bit Mono
Series 2 was taken using a Samsung YP55H MP3 recorder in 2004.
Sampling rate: WAV 8.00kHZ 8bit Mono


RWCP sound scene database
TRECVid home page


  1. Ma, L., Milner, B.P., Smith, D.J.  Acoustic Environment Classification, ACM Trans. on Speech and Language Processing 3(2), 1-22 DOI 10.1145/1149290.1149292, 2006.
  2. Smith, D.J., Ma, L., Ryan, N., Acoustic environment as an indicator of social and physical context, Personal and Ubiquitous Computing, 10(1), 2005.
  3. Steward, J., Using a PDA as an Audio Capture Device (pdf 485 KB), BSc Final Project, UEA School of Computing Sciences, 2005. 
  4. Ma, L., Smith, D.J. and Milner, B. Context Awareness using Environmental Noise Classification  (pdf 372 KB), Proc. Eurospeech 2003, Geneva, Switzerland, 2237-2240, 2003.
  5. Ma, L., Smith, D.J. and Milner, B. Environmental Noise Classification for Context-Aware Applications (pdf 254 KB), Proc. DEXA 2003, (LNCS 2736), 360-370, 2003.

Research Team

Dr. Dan Smith, Dr. Ben Milner, Ling Ma